Submissions

Biography:

Carlos Artemio Coello Coello received a Ph.D in Computer Science from Tulane University (USA) in 1996. His research has mainly focused on the design of new multi-objective optimization algorithms based on bio-inspired metaheuristics (e.g., evolutionary algorithms), which is an area in which he has made pioneering contributions. He currently has more than 500 publications, including more than 160 journal papers and 55 book chapters. He has published a monographic book and has edited 3 more books with publishers such as World Scientific and Springer. He has also published a book on the history of computing in Spanish, which has sold more than 3000 copies. He has supervised 21 PhD theses (including 3 in Argentina), 46 Masters thesis (including one in France) and 8 Undergraduate theses. Several of the PhD theses that he has supervised, have received awards in national competitions. He has also received (with his students) several “best paper awards” at different international conferences, including the prestigious Genetic and Evolutionary Computation Conference (GECCO), which is the most important in his field. He is also the only Latin American who has been awarded twice the “best paper award” of the IEEE Transactions on Evolutionary Computation. His publications currently report 52,662 citations in Google Scholar. According to Scopus, Dr. Coello has 18,782 citations, excluding self-citations and citations from all his co-authors. His h-index is 92, according to Google Scholar, 60 according to Scopus and 52 according to the ISI Web of Science. In the Shanghai Ranking’s Global Ranking of Academic Subjects 2016 developed by Elsevier, he appears as one of the 300 most highly cited scientists in the world in “Computer Science”, occupying the first place in Mexico.

He has received several awards, including the National Research Award (in 2007) from the Mexican Academy of Science (in the area of exact sciences), the 2009 Medal to the Scientific Merit from Mexico City's congress, the Ciudad Capital: Heberto Castillo 2011 Award for scientists under the age of 45, in Basic Science, the 2012 Scopus Award(Mexico's edition) for being the most highly cited scientist in engineering in the 5 years previous to the award and the 2012 National Medal of Science in Physics, Mathematics and Natural Sciences from Mexico's presidency (this is the most important award that a scientist can receive in Mexico). He also received the Luis Elizondo Award from the Instituto Tecnológico de Monterrey in 2019.

He is the recipient of the prestigious 2013 IEEE Kiyo Tomiyasu Award, "for pioneering contributions to single- and multi objective optimization techniques using bioinspired metaheuristics" and of the 2016 The World Academy of Sciences (TWAS) Award in “Engineering Sciences”. Since January 2011, he is an IEEE Fellow. He is also Associate Editor of several international journals including the IEEE Transactions on Evolutionary Computation, Evolutionary Computation and the IEEE Transactions on Emerging Topics in Computational Intelligence. He has been appointed as Editor-in-Chief of the IEEE Transactions on Evolutionary Computation for the period 2021-2022.

He is Full Professor with distinction (Investigador Cinvestav 3F) at the Computer Science Department of CINVESTAV-IPN in Mexico City, Mexico.

 

Topic:

An Overview of Evolutionary Multi-Objective Optimization
 

Abstract:

Multi-objective optimization refers to solving problems having two or more (often conflicting) objectives at the same time. Such problems are ill-defined and their solution is not a single solution but instead, a set of them, which represent the best possible trade-offs among the objectives. Evolutionary algorithms are particularly suitable for solving multi-objective problems because they are population-based, and require little domain-specific information to conduct the search. Due to these advantages, the development of the so-called multi-objective evolutionary algorithms (MOEAs) has significantly increased in the last 15 years. In this talk, we will provide a general overview of the field, including the main algorithms in current use as well as some of the many applications of them.

Biography:

First Indian to be invited by the OscarAcademy to join as a Member in Technical Category & appointed as Oscar Awards Jury for Lifetime since 2017.

First Indian to be invited by the Oscar Academy to join as a Member of The Academy’s Science & Technology Council in Oct 2020.

He has organized Oscar Academy President Mr.John Bailey’s historic First visit to India in May 2019.This visit has initiated new relationship between the Indian Film Industry & Oscar Academy.

He has initiated & executed translation of Oscar Academy’s Books on Digital Technology ”Digital Dilemma” into Marathi & Hindi languages for the benefit of Indian Film Industry.

Ujwal N.Nirgudkar is a Chemical Engineer from U.D.C.T. Mumbai University (Now called ICT) working with the Indian Film Industry for the past 39 years.

He started his career with Filmcenter Laboratory in Tardeo, Mumbai, as a Technical Manager in May 1981.When Filmcenter closed down in 2000, the same management started “Filmlab” at Goregaon in Mumbai. He was appointed as General Manager-Technical & was promoted to ‘Technical Director” of FILMLAB in July 2007.

He was also a Global Consultant to Fortune-500 Dutch MNC”Akzo Nobel”from 2007 to 2014 & promoted Green Technology for Film Processing around the world.

He is the First Indian to receive a US Patent for Motion Picture Technology in 2007.His patented technology is selected for a “Global Partnership Program” by TheU.K.Government.

He left Filmlab in May 2016 to join KPMG & is currently “Chief Technical Advisor” for “National Film Heritage Mission” a Rs.600 Croreproject, under the National Film Archives, Information & Broadcasting Ministry,Government of India.

He is the First Indian to be selected as a Fellow of SMPTE(Society of Motion Picture & Television Engineers)in 2007.

He established the SMPTE-India Section in Dec 2011 & Currently as a Chairman of SMPTE-India Section, he is promoting Standards for Digital Cinema in India.

 

Topic:

Contribution of Multimedia & Information Technology to overcome Communication Challenges during Pandemic
 

Abstract:

The current Covid-19 Pandemic has changed our way of life at home & workplace. In fact it has changed the definition of a workplace. The topic will cover how these innovative technologies will change the future of Education, Communication & Business Environment Worldwide.

Biography:

Dr. Sriparna Saha received the M.Tech and Ph.D. degrees in computer science from Indian Statistical Institute Kolkata, Kolkata, India, in 2005 and 2011, respectively. She is currently an Associate Professor in the Department of Computer Science and Engineering, Indian Institute of Technology Patna, India. She is the author of a book published by Springer-Verlag. She has authored or coauthored more than 223 papers. Her current research interests include deep learning, natural language processing, machine learning, information extraction, text mining, bioinformatics and multiobjective optimization. Her h-index is 26 and the total citation count of her papers is 4262 according to Google scholar). She is also a senior member of IEEE. She is the recipient of the Lt Rashi Roy Memorial Gold Medal from the Indian Statistical Institute for outstanding performance in MTech (computer science). She is the recipient of the Google India Women in Engineering Award, 2008, NASI YOUNG SCIENTIST PLATINUM JUBILEE AWARD 2016, BIRD Award 2016, IEI Young Engineers' Award 2016, SERB WOMEN IN EXCELLENCE AWARD 2018, and SERB Early Career Research Award 2018. She is the recipient of DUO-India fellowship 2020, Humboldt Research Fellowship, Indo-U.S. Fellowship for Women in STEMM (WISTEMM) Women Overseas Fellowship program 2018 and CNRS fellowship. She had also received the India4EU fellowship of the European Union to work as a Post-doctoral Research Fellow at the University of Trento, Italy from September 2010-January 2011.

 

Topic:

Multimodal Summarization Techniques
 

Abstract:

The new era of technology has brought us to the point where it is very convenient for a person to share their opinions, and to have an abundance of platforms to gain information from. Every such platform of information sharing, be it newspapers, television, websites, micro-blogs, social media, etc., has a provision for the content creators to express their thoughts and convey information in multiple forms of representations, including text, images, videos, and audio. This, however, makes it difficult for users to gain all the information about a topic, making the task of automatic multi-modal summarization (MMS) essential. I will talk in detail about some MMS systems which have explored integer linear programming, multiobjective optimization concepts. Important objectives such as intra-modality salience, cross-modal redundancy, and cross-modal similarity are optimized simultaneously in a multi-objective optimization framework to produce effective multi-modal output. The proposed model has been evaluated separately for different modalities and has been found to perform better than state-of-the-art approaches. In a part of the talk, concepts of microblog summarization will also be described. Tweet-texts along with tweet images can be utilized for generating the microblog summarization.

Biography:

He is currently a Professor of computer science at the Institute of Digital Media, EECS, Peking University, Beijing, China. He received his PhD degree in computer science from Institute of Computing Technology, Chinese Academy of Sciences, in 2005. He held a Postdoctoral position with the University of Southern California, Los Angeles, CA, USA, 2005 to 2007. Then he joined Peking University until now. His research interests include image and video coding, video processing, video streaming, and transmission. He served/serves as an Associate Editor of the IEEE Transaction on Circuits and Systems for Video Technology and the Journal of Visual Communication and Image Representation. He is the chair of IEEE 1857 standard workgroup and the chair of AVS video group.

 

Topic:

IEEE 1857.10----The Third Generation IEEE 1857 Video Coding Standard

 

Biography:

Snehanshu Saha is an Associate Professor of Computer Science and Anuradha and Prashanth Palakurthi Centre for Artificial Intelligence Research (APPCAIR) since 2019 and heads the Center for AstroInformatics Modeling and Simulation (CAMS). He has published more than 90 peer-reviewed articles in reputed International journals and conferences. Dr. Saha is an IEEE Senior member, ACM Senior Member and Fellow-IETE. Dr. Saha is the Editor of Journal of Scientometric Research. Dr. Saha received distinguished researcher award, PEACE in AstroInformatics and Machine Learning in 2019. His current and future research interests lie in Computational Learning Theory, Mathematics of Data Science and AstroInformatics.

 

Topic:

From Small Data to Big Data: The Evolution of AI techniques
 

Abstract:

Pattern Recognition in the classical sense has seen major transformation in recent times owing to the implosion in the volume of data. The velocity and variety in data triggers changes in architectural approaches with less emphasis on the methodological elegance and the traditional beliefs in bias-variance tradeoffs. The changed Landscape of drawing inferences from Small Data Pattern Recognition (SDPR) is inflicting cost on the prediction reliability-What did we miss? Can the elegance be ignored? I'll present a (mild ?) critique of Deep Learning inferences in Big Data and stress on the re-emergence of (Methodological) elegance in Big Data Pattern Recognition (BDPR) by discussing sophisticated techniques which can be ported from SDPR.

Biography:

Santonu Sarkar is a Senior Principal Scientist at ABB Corporate Research Bangalore and an adjunct professor of Computer Sc and Information Systems Dept. BITS Pilani, K.K.Birla Goa Campus. Dr. Sarkar received the Ph.D. degree in computer science from Indian Institute of Technology Kharagpur. He has more than 25 years of experience in the IT industry, applied research, product & application development, architecture consulting, project and client account management. He is currently working on engineering automation and design verification of industrial automation systems. His research interest includes building software engineering techniques to ensure dependability, performance, and ease-of-use of Cloud, Cyber-physical systems and HPC applications. Before this, he had extensively worked in different fields of software engineering, namely in the area of software metrics and measurement, software design and architecture analysis, program comprehension, and reengineering techniques. His other research interests include analysis of social networking data. Dr. Sarkar has total 15 granted patents and several publications in the peer-reviewed journals and conferences with h and i10 indices of 20 and 35 respectively.

 

Topic:

Industry 4.0: Digital Twin Driven Modeling and Simulation of Industrial Cyber-physical Systems
 

Abstract:

Michael Grieves et al. defined the term Digital Twin as the one that mirrors a physical product based on integrated multi-physics, multi-scale simulation. In recent times, digital twin concept has gained momentum in various industrial applications such as in manufacturing, utilities, oil and gas, mining, process automation and so on. A digital twin defines a model based on the multi-physics model, data-driven AI/ML model, and discrete models. In this talk, we give an overview of how the industry is embracing the digital twin in its Industry 4.0 journey. We provide an overview of various modelling approaches and specifically focus on discrete models. We discuss multiple simulation scenarios and how digital twin based simulations are useful in analyzing various characteristics of the physical system.